A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor

Stefano Berrone; Davide Oberto

doi:10.1063/5.0104605

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arXiv · 2203.16944

A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor

Stefano Berrone·Davide Oberto

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Abstract

In the present paper a new data-driven model is proposed to close and increase accuracy of RANS equations. The divergence of the Reynolds Stress Tensor (RST) is obtained through a Neural Network (NN) whose architecture and input choice guarantee both Galilean and coordinates-frame rotation. The former derives from the input choice of the NN while the latter from the expansion of the divergence of the RST into a vector basis. This approach has been widely used for data-driven models for the anisotropic RST or the RST discrepancies and it is here proposed for the divergence of the RST. Hence, a constitutive relation of the divergence of the RST from mean quantities is proposed to obtain such expansion. Moreover, once the proposed data-driven approach is trained, there is no need to run any classic turbulence model to close the equations. The well-known tests of flow in a square duct and over periodic hills are used to show advantages of the present method compared to standard turbulence models.

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Stefano Berrone, Davide Oberto. 2022-03-31. A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor. https://doi.org/10.1063/5.0104605

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